Computationally, we only used averaging of the mutual information among models to design optimal experiments. Weighting each model's mutual information by its respective marginal probability may lead to improved designs for for the model with more evidence. We used a simple ensemble method to evaluate the performance of iid models using our algorithm, which allows us to measure uncertainty in models' predictions. Mixtures of Experts (MoEs) [1]}, [2]} have also shown promise to improve training and can be combined with ensembling methods to also perform uncertainty quantification [3]}. These methods could both improve performance and uncertainty quantification in optimal designs for biological models.
